Marginal Models: Interpretation and Application

This lecture discusses marginal models in the context of modern regression, focusing on the interpretation and application of these models. It covers the formulation of joint log-linear models, the analysis of visual impairment data, and the assessment of fit for overdispersion. The instructor explains the statistical drawbacks of log-linear models, the implications of marginal probabilities on visual impairment, and the methods for dealing with overdispersion in data analysis.